Computational analysis in spatial transcriptomics: methods and perspectives.

Authors

Qin Zhou, Yi Jiang, Peifeng Ruan, Guanghua Xiao, Yang Xie

Published in

Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.

Abstract

Spatial transcriptomics (STs) enables spatially resolved gene-expression profiling across diverse tissues, generating large-scale datasets that integrate molecular and spatial information. To analyze and interpret the ST data, a wide range of computational methods have been proposed. As a result of continued expansion in the quantity and complexity of the methods for ST analysis, there is an increasing need for clear and structured guidance to help researchers effectively apply appropriate strategies in their study. In this review, we present a comprehensive overview of the current state of computational approaches in ST data analysis, covering key aspects concerning data storage, data preprocessing, resolution enhancement, and downstream analyses, including spatial domain identification, spatially variable genes detection, cell type annotation, cell-cell communication, gene expression prediction modeling, and 3D ST reconstruction. Across these areas, we summarize recent methodological advances, highlight remaining challenges, and outline future directions for advancing ST analysis, particularly in computational methods related to high-resolution ST platforms.

PMID:
42555503
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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